Systems and methods for efficently sensing collison threats

ABSTRACT

A system for efficiently sensing collision threats has an image sensor configured to capture an image of a scene external to a vehicle. The system is configured to then identify an area of the image that is associated with homogeneous sensor values and is thus likely devoid of collision threats. In order to reduce the computational processing required for detecting collision threats, the system culls the identified area from the image, thereby conserving the processing resources of the system.

RELATED ART

Aircraft may encounter a wide variety of collision threats duringflight, such as debris, other aircraft, equipment, buildings, birds,terrain, and other objects. Collision with any such object may causesignificant damage and/or injury to an aircraft and its occupants.Sensors may be used to detect objects that pose a collision risk andwarn a pilot of detected collision risks. In a self-piloted aircraft,sensor data indicative of objects around the aircraft may be usedautonomously to avoid collision with the detected objects.

To ensure safe and efficient operation of an aircraft, it is desirableto detect objects in all of the space around the aircraft. However,detecting objects around an aircraft and determining a suitable path forthe aircraft to follow in order to avoid colliding with the objects canbe challenging. Systems capable of performing the assessments needed toreliably detect and avoid objects external to the aircraft may beburdensome and computationally expensive to implement.

To illustrate, a self-piloted aircraft may have, on its exterior, alarge number of image sensors, such as cameras, that provide sensorreadings for full, 3-dimensional coverage of the spherical areasurrounding the aircraft. The data collected from these image sensorsmay be processed by one or more processors (e.g., CPUs) implementingvarious algorithms to determine whether an image captured by a cameradepicts a collision threat. Further, to facilitate detection ofcollision threats, high resolution cameras may be used, and the amountof data from a large number of high-resolution cameras can besignificant and consume an extensive amount of processing resources.Systems and methods for reducing the processing burdens associated withthe detection of collision threats without compromising safety or systemrobustness are generally desired.

BRIEF DESCRIPTION OF THE DRAWINGS

The disclosure can be better understood with reference to the followingdrawings. The elements of the drawings are not necessarily to scalerelative to each other, emphasis instead being placed upon clearlyillustrating the principles of the disclosure. Furthermore, likereference numerals designate corresponding parts throughout the severalviews.

FIG. 1 depicts a vehicle with multiple sensors.

FIG. 2 depicts an image of a scene external to the vehicle depicted byFIG. 1 .

FIG. 3 is a block diagram illustrating an exemplary embodiment of asystem for sensing collision threats.

FIG. 4 is a block diagram illustrating an exemplary embodiment of afilter, such as is depicted by FIG. 3 .

FIG. 5 is a block diagram illustrating an exemplary embodiment of anobject detector, such as is depicted by FIG. 3 .

FIG. 6 is a diagram illustrating an exemplary process for detectingcollision threats.

FIG. 7 depicts an exemplary histogram of a homogeneous sensor values.

FIG. 8 depicts an exemplary histogram of inhomogeneous sensor values.

FIG. 9 depicts an exemplary scene external to a vehicle.

FIG. 10 is a block diagram illustrating an exemplary embodiment of asystem for sensing collision threats.

DETAILED DESCRIPTION

The present disclosure generally pertains to systems and methods forefficiently sensing collision threats. A system in accordance with oneembodiment of the present disclosure is configured to capture an imageof a scene external to a vehicle and to then identify and cull areas oflow interest (e.g., areas associated with homogeneous sensor values)from the image to reduce the computational processing needed fordetecting collision threats. In this regard, such areas likely have nocollision threats, and the system therefore does not need to usurpprocessing resources analyzing the areas. Thus, the total amount ofimage data needed to be analyzed for identifying collision threats isreduced without compromising safety. Considering that a vehicle, such asan autonomous aircraft, might utilize a large number of high-resolutionimage sensors, a significant amount of processing resources may beconserved by culling areas associated with homogeneous sensor values,referred to hereafter as “homogeneous areas.”

In some embodiments, a vehicle has an image sensor (e.g., one or morecameras) that captures at least one image of an area or scene externalto the vehicle. The image sensor is configured to feed images to afilter that identifies homogeneous areas within the images. An area ofan image (which corresponds to a geographic region of the imaged scene)may be determined to be homogeneous by comparing sensor values (e.g.,pixels) associated with the area. In this regard, the area may beconsidered to be “homogenous” if such values are substantially similarindicating that there is not likely a collision threat within thegeographic region represented by such area. A filter is used to cullareas of the image determined to be homogenous such that the homogenousareas are not processed by an object detector that is configured toanalyze the image for the purpose of identifying collision threats.

In some embodiments, conventional segmentation techniques may be used tofacilitate detection of homogeneous areas. As an example, a frame ofimage data may be first segmented and resulting image segments may thenbe checked for homogeneousness. Such determinations may be made bycomparing pixel values in an image segment to determine whether they aresufficiently similar such that an image of a collision threat is notlikely within the segment. In some embodiments, homogeneousnessdeterminations may be made by analyzing histograms of the segment. Inother embodiments, a homogeneous areas may be identified with the use ofanother sensor, such as another image sensor, a radar sensor, or a lightdetection and ranging (LiDAR) sensor. In this regard, sensor data fromsuch other sensor may be analyzed to identify a region within the sceneassociated with homogenous sensor values. The area of a captured imagecorresponding to this same region may be identified as a homogeneousarea of the image and, therefore, culled as described above. Thus, anarea of an image may be identified as “homogeneous” by analyzing thepixel values of the image or sensor data from other sensorscorresponding to the same geographic region.

In some embodiments, the vehicle has a user, such as a pilot or driverwho manually controls operation of the vehicle. In such case, the objectdetector may identify collision threats and provide informationindicative of such collisions threats to the user who may then use theinformation to control the vehicle, such as steering the vehicle toavoid the identified collision threats. In other embodiments, thevehicle may be self-piloted or, in other words, autonomous. In suchcase, the object detector may provide information indicative ofidentified collision threats to a control system for autonomouslycontrolling operation of the vehicle, and the control system can usesuch information to control the vehicle, such as steering the vehicle toavoid the identified collision threats. Other uses of the identifiedcollision threats are possible in other embodiments.

FIG. 1 depicts an exemplary embodiment of a vehicle having a system fordetecting collision threats. FIG. 1 depicts a view of a vehicle 10having a plurality of image sensors 30, each image sensor 30 having itsown field of view 20. While FIG. 1 depicts an airplane, the principlesof the disclosure may be applied to any vehicle (e.g., boat, car, truck,and other types of aircraft, such as drone or a helicopter). While FIG.1 depicts four sensors 30, any number of sensors 30 are possible inother embodiments. The sensors 30 may be directed in specific fixeddirections with respect to the vehicle 10 or may be mounted in such away to allow for panning or rotating of the sensors' fields of view 20to different angles and directions with respect to the vehicle 10. Eachsensor 30 is configured to capture one or more images of the externalsurroundings for use in detecting collision threats, as will bedescribed in more detail below. As an example, each image sensor 30 maybe a video camera that provides a video stream comprising frames ofimage data, where each frame defines a captured image for the sensor'sfield of view. As known in the art, each image frame may be a collectionof pixels, each pixel having a respective intensity value indicative ofa color and brightness of light reflected from a respective geographicpoint within the sensor's field of view.

FIG. 2 depicts a captured image 200 of a scene that is external to thevehicle 10 within the field of view 20 of image sensor 30 (not shown inFIG. 2 ) that captured the image 200. An image 200 may depict the sky280, the ground 290, or some combination thereof. The sky 280 mayinclude one or more vast, open spaces 220, clouds 210, birds (notshown), other vehicles 10, or other objects. The ground 290 may includemountains 240, fields 250, 260, 270 (e.g., grass, dirt, crops, etc.),buildings (not shown), lakes (not shown), rivers (not shown), or othersurface features.

Sensed values, such as pixel values within a captured image, forreflections from many types of objects, such as collision threats, areoften inhomogeneous. In this regard, a typical collision threat, such asanother vehicle, often exhibits different contours and colors across avisible surface of the collision threat. Thus, the sensed value for areflection from one portion of the collision threat will often varydrastically from the sensed value from another portion of the collisionthreat. Therefore, if a relatively large area of a captured image can beassociated with sensed values that are substantially homogenous, then itcan be safely assumed that such area is devoid of collision threats. Forexample, if no collision threat is within the sky space 220, then thesensed values for reflections from this space 220 may be homogenous.However, if the sensed values are inhomogeneous, then it is possiblethat the sensed values may be indicative of a collision threat.

As an example, if the sky space 220 is devoid of collision threats, thenthe portion of the image 200 corresponding to the sky space 220 may havevalues that are substantially homogenous (e.g., the intensity values forthe pixels representing the sky space 220 may be about the same, such asa particular shade of blue). Similarly, if no collision threat isbetween the vehicle 10 and the field 260, then the sensed values forreflections this field 260 may be homogenous. As an example, the portionof the image 200 corresponding to the field 260 may have values that aresubstantially homogenous (e.g., the intensity values for the pixelsrepresenting the field 260 may be about the same, such as a particularshade of green). These homogeneous areas of the image 200 can beidentified and culled so that they are not processed for detectingcollision threats. Significant computing resources and power can besaved by not processing these areas of a scene's image, particularlywhen such savings are realized for a large number of images.

FIG. 3 is a block diagram illustrating an exemplary embodiment of asystem 100 for detecting collision threats. The system 100 may reside onthe vehicle 10 and has at least one image sensor 30 that captures atleast one image of scene and provides image data defining the capturedimage to a filter 40. The filter 40 is configured to cull at least someof the image data to reduce the overall size of the image data definingthe captured image and provide the filtered data to an object detector50, which may analyze the filtered data to detect collision threats. Theobject detector 50 may include one or more processors that implementmachine learning algorithms (e.g., deep learning pipelines) or othertechnologies for detecting, identifying, and classifying objects thatmay be collision threats to the vehicle 10. Such detection may includedetermining if a detected object is on a collision course with theintended path of the vehicle 10 or is likely to come sufficiently closeto the vehicle 10 to be threat to the safe operation of the vehicle 10.Information regarding the detected objects can then be passed to anoutput interface 60 or a vehicle control system 70.

The vehicle control system 70 may be configured to control operation ofthe vehicle 10 based on the information from the filter 40. As anexample, in an autonomous vehicle (e.g., self-piloted or self-driven),the vehicle 10 may include one or more processors that provide controlinputs for controlling the vehicle 10 to steer it in a direction toavoid a collision with the collision threat detected by the objectdetector 50. Exemplary configurations and operations of the objectdetector 50 and vehicle control system are described in U.S. patentapplication Ser. No. 16/611,427, entitled “Systems and Methods forSensing and Avoiding External objects for Aircraft” and filed on Nov. 6,2019, which is incorporated herein by reference.

In operator-controlled vehicles, the output interface 60 may beconfigured to display or otherwise output information from the filter 40about detected collision threats. As an example, the output informationmay display or otherwise output warnings indicative of detectedcollision threats so that a user, such as a pilot or driver of thevehicle 10, may use such information to control the vehicle 10. Suchinformation may also be displayed to a user in an autonomous vehicle,such as a user who may optionally assume control of the vehicle 10 toavoid collision threats or perform other maneuvers.

The filter 40 may be implemented in specialized hardware (e.g., a FGPAor ASIC, or other appropriate type of analog or digital circuits),hardware (e.g., one or more processors) executing software, or somecombination thereof. FIG. 4 is a diagram illustrating an exemplaryembodiment of a filter 40. The filter 40 depicted by FIG. 4 has at leastone processor 410, memory 420, and data interface 480. These componentsmay communicate with one another through a local interface 470 (e.g., asystem bus). Memory 420 may contain raw sensor data 430 (e.g., images)or some portion of the sensor data stream received from one or moresensors 30, filter logic 440, and filtered data 460.

The processor 410 may include hardware for executing instructions,(e.g., instructions from the filter logic 440) such as a centralprocessing unit (CPU), a digital signal processor (DSP), a graphicsprocessing unit (GPU), an FPGA, or other types of processing hardware,or any combination thereof. The processor 410 may be configured toexecute instructions stored in memory 420 in order to perform variousfunctions, such as processing of raw sensor data 430 from the imagesensors 30.

The filter logic 440 is configured to cull portions of the raw sensordata 430, such as areas that the logic 440 determines are likely free ofcollision threats. Such culling may include removing such portionsaltogether from the raw sensor data 430 or in some embodiments insteadof removing the data indications may be stored indicating portions ofthe raw sensor data 430 that may be ignored by the object detector 50.Exemplary techniques for culling the raw sensor data 430 will bedescribed in more detail below. Regardless of the type of cullingperformed, the culling effectively prevents the object detector 50 fromanalyzing the culled data for the purpose detecting collision threats,thereby reducing the processing burdens of the object detector 50.

The object detector 50 may be implemented in specialized hardware (e.g.,a FGPA or ASIC, or other appropriate type of analog or digitalcircuits), hardware (e.g., one or more processors) executing software,or some combination thereof. FIG. 5 is a diagram illustrating anexemplary embodiment of an object detector 50. The object detector 50depicted by FIG. 5 has at least one processor 510, memory 520, and datainterface 580. These components may communicate with one another througha local interface 570 (e.g., a system bus). Memory 520 may containfiltered data 460 received from the filter 40 and object detection logic530.

The object detection logic 530 is configured to process the filtereddata 460 looking for external objects relevant to the control of thevehicle 10. Such objects may include collision threats (e.g., birds,other aircraft, debris, towers, buildings, etc.). Information regardingdetected objects may be sent to the vehicle control system 70 and/or theoutput interface 60. The object detection logic 530 may be implementedin many ways including machine-learning algorithms that analyze thefiltered data for detecting and classifying objects that may becollision threats. In some embodiments, the filter 40 and the objectdetector 50 may share resources. Such resources may include one or moreprocessors and/or memory. For example, the same processors or group ofprocessors used to identify an area of an image associated withhomogeneous sensor values and cull such area to provide a filtered imagemay also be used to analyze the filtered image to detect collisionthreats.

FIG. 6 is a diagram illustrating an exemplary process for detectingcollision threats. At Step 610, the filter 40 receives raw sensor data430 from one or more image sensors 30. At Step 620, the filter 40 cullsareas of an image associated with homogeneous sensor values. In someembodiments raw sensor data 430 defining an image captured by an imagesensor 30 may be segmented in order to facilitate identification ofareas to be culled. Segmentation may be done in a variety of waysincluding but not limited to using edge detection or subdividing theimage into fixed size blocks (e.g., 10 by 10, 20 by 20, 30 by 40, etc.).In some embodiments, segmentation may be performed such that pixelshaving a similar color, intensity or contrast pattern or distributionare grouped to form a segment to be analyzed. Various known segmentationalgorithms may be used to divide an image into one or more segments.

Each segment can then be analyzed by the filtering logic 440 todetermine whether the segment is homogeneous. As an example, for eachsegment, the filtering logic 440 may compare the pixels of the segment.If a sufficiently high number of pixels have intensity values within acertain range of each other, then the filtering logic 440 may beconfigured to identify the segment as a homogenous area of the image tobe culled in step 620. Note that a variety of techniques may be used todetermine whether a segment of an image is homogeneous.

FIG. 7 depicts an exemplary histogram of a homogeneous segment. As isshown most if not all the pixels are within a tight range of intensityas evidenced by a high impulse peak in the curve. FIG. 8 depicts anexemplary histogram of an inhomogeneous segment. As shown by FIG. 8 ,the curve is characterized by a relatively large number of small peaksspread across the intensity axis. None of the peaks in FIG. 8 are nearlyas high as the single peak depicted by FIG. 7 . There are varioustechniques that can be used to determine whether the intensity values ofa segment have a signature indicative of homogeneity.

For example, the filter logic 440 may be configured to calculate orotherwise determine the average intensity value of a segment then checkto determine how many or what percentage of the pixels have intensityvalues within a predefined threshold of the average value. In such anexample, such number or percentage of the pixels is indicative of thehomogeneity of the sensor values, and this value may be compared to athreshold to determine whether the segment should be considered to behomogeneous. In this regard, if the value exceeds the threshold, thenthe filter logic 440 may determine that the segment is indeedhomogeneous and, therefore, cull it from the image.

Referring again to FIG. 6 , at Step 620, segments or other image areasidentified to be homogeneous are culled to provide filtered data 460,and at Step 630, the filtered data 460 is passed to the object detector50. Objects including potential collision threats may be identified bythe object detector 50. In circumstances where raw sensor data 430 hasbeen culled by the filter 40, the processing burden on the objectdetector 50 is reduced. At Step 640, information regarding the detectedobjects is passed to the output interface 60 and/or the vehicle controlsystem 70.

In some embodiments, one or more sensors 30 may face downwards. FIG. 9depicts a scene 200 external to the vehicle 10 with the sensor 30directed downward towards the ground 290. Some areas of the scene 200may include a tower 820, buildings 830, and other obstacles 840. Asstated earlier, the views of the ground 290 may include large areas 260(e.g., grasslands, calm lakes, etc.) that appear homogeneous in thesensor data. As an example, a segment of an image of a large, opengrassland may have intensity values that are substantially homogeneoussuch that the segment can be culled by the filter 40, as describedabove.

As indicated above, there are various techniques that may be used todetermine when a portion of an image is associated with homogeneoussensor values. Indeed, as noted above, this can be achieved by analyzingthe image to find an area of the image having homogeneous pixel values.This technique can be used to identify relatively large areas having lowentropy indicative of an absence of collision threats, such as a segmentof sky or grasslands where pixels have substantially similar intensityvalues. However, other techniques are possible. For example, in someembodiments, a sensor other than the image sensor 30 that provided theimage being processed may be used to identify a portion of the image tobe culled.

FIG. 10 depicts an exemplary embodiment in which a sensor 130 differentthan the image sensor 30 providing the image being processed is used toidentify an area of the image associated with homogeneous sensor values.The sensor 130 may be another image sensor, such as a camera, or othertype of sensor, such as radar or LiDAR. The sensor 130 is configured toprovide sensor data for the same geographic region imaged by the imagesensor 30. As an example, if the sensor 130 is a camera, the sensor 130may receive light reflected from the same geographic region imaged bythe image sensor 30. That is, the fields of view of the image sensor 30and the sensor 130 overlap. If the sensor 130 is a radar or LiDARsensor, then the sensor 130 may be configured to transmit a signal andreceive reflections of the signal from same geographic region for whichthe image sensor 30 receives reflections of light.

The filter 40 is configured to analyze the sensor data from the sensor130 to identify a homogenous grouping of sensor values, similar to thetechniques described above for the image data from the image sensor 30.For example, if the sensor 130 is a radar or LiDAR sensor, it isexpected that measurements of the returns from a homogenous area, suchas a flat field or the sky in the absence of objects between the vehicle10 and the homogeneous area, should be substantially similar. Similarly,if the sensor 130 is an image sensor, such as a camera, it is expectedthat measurements of light from a homogeneous area, such as a field orsky having a substantially uniform color, should be substantiallysimilar. Thus, the sensor data from the sensor 130 can be analyzed toidentify a geographic region that is associated with homogeneous sensorvalues using techniques similar to those described above for identifyingan area of image from sensor 30 to be associated with homogeneous pixelvalues.

After identifying a geographic area associated with homogeneous sensorvalues based on the sensor 130, the filter 40 may identify the samegeographic area in the image received from the image sensor 30. That is,the filter 40 may correlate the geographic region identified from thesensor data provided by the sensor 130 with the same geographic regionin the image received from the sensor 30. Thus, the filter 40 identifiesthe pixels of the image from the sensor 30 that are representative ofthe same geographic region for which the sensor data from sensor 130indicated to be homogeneous. The filter 40 may then cull such pixelvalues from the image, thereby reducing the amount of data that must beprocessed by the object detector 50 to analyze the image.

Notably, using the sensor data from the sensor 130 to identify ahomogeneous area of an image to be culled may enable the filter 40 tocull at least some image data that otherwise might not be identified ashomogenous based solely on the image data from sensor 30. As an example,a field may be substantially flat but have drastically varying colorsacross its surface. Such a field may not appear to have homogeneouspixel values in the image from the sensor 30 but may have homogeneoussensor values in the data from the sensor 130. Similarly, a region ofthe sky may have differing intensity values due to clouds, pollution, orvarying lighting conditions, but such a region may be associated withhomogeneous sensor values from the senor 130, such as radar or LiDARvalues. In yet other examples, other types of sensors may be used toimplement the sensor 130 and provide sensor values that may indicatehomogeneous areas that can be culled from the image being processed bythe filter 40.

The foregoing is merely illustrative of the principles of thisdisclosure and various modifications may be made by those skilled in theart without departing from the scope of this disclosure. The abovedescribed embodiments are presented for purposes of illustration and notof limitation. The present disclosure also can take many forms otherthan those explicitly described herein. Accordingly, it is emphasizedthat this disclosure is not limited to the explicitly disclosed methods,systems, and apparatuses, but is intended to include variations to andmodifications thereof, which are within the spirit of the followingclaims.

As a further example, variations of apparatus or process parameters(e.g., dimensions, configurations, components, process step order, etc.)may be made to further optimize the provided structures, devices andmethods, as shown and described herein. In any event, the structures anddevices, as well as the associated methods, described herein have manyapplications. Therefore, the disclosed subject matter should not belimited to any single embodiment described herein, but rather should beconstrued in breadth and scope in accordance with the appended claims.

Now, therefore, the following is claimed:
 1. A vehicular system forsensing collision threats, comprising: a vehicle; an image sensorcoupled to the vehicle and configured to capture a first image of ascene external to the vehicle; a filter configured to receive the firstimage and identify an area of the first image associated withhomogeneous sensor values, the filter further configured to cull theidentified area from the first image thereby providing a filtered image;and an object detector configured to receive the filtered image andprocess the filtered image to identify a collision threat within thefiltered image, the object detector further configured to provideinformation indicative of the detected collision threat.
 2. Thevehicular system of claim 1, wherein the object detector implements amachine learning algorithm for processing the filtered image to identifythe collision threat.
 3. The vehicular system of claim 1, wherein thehomogeneous sensor values comprise pixels of the first image.
 4. Thevehicular system of claim 1, further comprising a second image sensorcoupled to the vehicle and configured to capture a second image of thescene, wherein the homogeneous sensor values comprise pixels of thesecond image.
 5. The vehicular system of claim 1, further comprising asecond sensor coupled to the vehicle and configured to provide thehomogeneous sensor values based on reflections from the scene, whereinthe filter is configured to correlate the homogeneous sensor values withthe area of the first image.
 6. The vehicular system of claim 5, whereinthe second sensor is a radar sensor.
 7. The vehicular system of claim 5,wherein the second sensor is a light detection and ranging (LiDAR)sensor.
 8. The vehicular system of claim 1, wherein the filter isconfigured to determine a value indicative of a homogeneity of thehomogeneous sensor values and compare the value to a threshold.
 9. Avehicular system for sensing collision threats, comprising: a vehicle;an image sensor coupled to the vehicle and configured to capture a firstimage of a scene external to the vehicle; at least one processorconfigured to receive the first image, the at least one processorprogrammed with instructions that, when executed by the at least oneprocessor, cause the at least one processor to: determine whether anarea of the first image is associated with homogenous sensor values; ifthe area is determined to be associated with homogeneous sensor values,cull the area from the first image, thereby providing a filtered image;detect a collision threat for the vehicle based on the filtered image;and provide information indicative of the detected collision threat. 10.The vehicular system of claim 9, wherein the homogeneous sensor valuescomprise pixels of the first image.
 11. The vehicular system of claim 9,further comprising a second image sensor coupled to the vehicle andconfigured to capture a second image of the scene, wherein thehomogeneous sensor values comprise pixels of the second image.
 12. Thevehicular system of claim 9, further comprising a second sensor coupledto the vehicle and configured to provide the homogeneous sensor valuesbased on reflections from the scene, wherein the filter is configured tocorrelate the homogeneous sensor values with the area of the firstimage.
 13. The vehicular system of claim 12, wherein the second sensoris a radar sensor.
 14. The vehicular system of claim 12, wherein thesecond sensor is a light detection and ranging (LiDAR) sensor.
 15. Thevehicular system of claim 9, wherein the filter is configured todetermine a value indicative of a homogeneity of the homogeneous sensorvalues and compare the value to a threshold.
 16. A method for sensingcollision threats, comprising: capturing a first image of a sceneexternal to a vehicle with a first image sensor; identifying, with atleast one processor, an area of the first image associated withhomogeneous sensor values; culling the area from the first image withthe at least one processor; analyzing the first image subsequent to theculling with the at least one processor; detecting a collision threat tothe vehicle with the at least one processor based on the analyzing; andproviding, with the at least one processor, information indicative ofthe detected collision threat.
 17. The method of claim 16, wherein thehomogeneous sensor values comprise pixels of the first image.
 18. Themethod of claim 16, further comprising capturing a second image of thescene with a second image sensor, wherein the homogeneous sensor valuescomprise pixels of the second image.
 19. The method of claim 16, furthercomprising: providing, with a second sensor, the homogeneous sensorvalues based on reflections from the scene; and correlating, with the atleast one processor, the homogeneous sensor values with the area of thefirst image.
 20. The method of claim 19, wherein the second sensor is aradar sensor.
 21. The method of claim 19, wherein the second sensor is alight detection and ranging (LiDAR) sensor.
 22. The method of claim 16,wherein further comprising: determining a value indicative of ahomogeneity of the homogeneous sensor values; and comparing the value toa threshold.